We are close to the publishing day of the T-SQL Querying book. Of course, like always in this series, the main author of the book is Itzik Ben-Gan. This time, besides me, Adam Machanic and Kevin Farlee are the coauthors. The information I want to share now is that you can get a substantial discount if you preorder the book today, Monday, February ...

I was just starting to work on a post on column statistics, using one of my favorite metadata functions: sys.dm_db_stats_properties(), when I realized something was missing.
The function requires a stats_id value, which is available from sys.stats. However, sys.stats does not show the column names that each statistics object is attached ...

I spent the last few days in Zagreb, Croatie, at the third edition of the SQL TuneIn conference, and I had a very good time here. Nice company, good sessions, and awesome audiences.
I presented my “Understanding Execution Plans” precon to a small but interested audience on Monday. Participants have received a download link for the slide deck.
On ...

There are two ways to test how your queries behave on huge amounts of data. The simple option is to actually use them on huge amounts of data – but where do you get that if you have no access to the production database, and how do you store it if you happen not to have a multi-terabyte storage array sitting in your basement? So here’s the second ...

The session Skewed Data, Poor Cardinality Estimates, and Plans Gone Bad by Kimberly Tripp (@KimberlyLTripp) has been published on channel SQLPASS TV. Abstract When data distribution is heavily skewed, cardinality estimation (how many rows the query optimizer expects each operator to process) can be wildly incorrect, resulting in ...

This is the fifth, the final part of the fraud detection whitepaper. You can find the first part, the second part, the third part, and the fourth part in my previous blog posts about this topic. The Results In my original fraud detection whitepaper I wrote for SolidQ, I was advised by my friends to include some concrete and simple numbers to ...

This is the fourth part of the fraud detection whitepaper. You can find the first part, the second part, and the third part in my previous blog posts about this topic. Data Mining Models We create multiple mining models by using different algorithms, different input data sets, and different algorithm parameters. Then we evaluate the models in ...

This is the third part of the fraud detection whitepaper. You can find the first part and the second part in my previous blog posts about this topic. Data Preparation The problem of credit card fraud detection is not trivial. With every transaction processed, only a limited amount of data is available, making it difficult if not impossible to ...

Happy Fall! It’s a beautiful October here in Minneapolis / Saint Paul. In preparation for my home town SQL Saturday this weekend, as well as the PASS Summit, I offer an update to the Rules-Driven Maintenance code I originally published back in August 2012. It’s hard to believe this thing is now more than two years old – it’s been an incredible ...

I have been working on new features for the Rules-Driven Maintenance Solution, including per-index maintenance preferences and more selective processing for statistics. SQL Server stats is a topic I knew about at a high level, but lately I have had to take a deeper dive into the DMVs and system views and wrap my head around things like ...